AI-powered testing platform. Write tests in plain English, run them everywhere.
Test Automation
Autify and Testim both position themselves as AI-powered no-code test automation platforms. Both use machine learning to make tests resilient to UI changes. Both target QA teams that want to reduce manual maintenance burden without switching to code-first frameworks. But their design philosophies, execution models, and team
Test Automation
QA teams are constantly caught between two realities: testing backlogs grow faster than automation engineers can write scripts, and every UI change breaks half the test suite overnight. Autify was built to address exactly these pain points — a no-code AI-powered test automation platform that lets anyone on the
Test Automation
Mobile app testing has always been harder than web testing. You are dealing with two major platforms (iOS and Android), dozens of OS versions, hundreds of device models, and a recorder workflow that must work with native UI components rather than HTML elements. Autify Mobile was built to bring the
Test Automation
The most expensive part of UI test automation is not writing tests — it is keeping them working. A study often cited in the QA industry estimates that 25-30% of automated test maintenance effort goes to updating tests after UI changes that have nothing to do with application bugs. An
BDD
Acceptance Test-Driven Development fails more often from collaboration problems than technical ones. Teams adopt Cucumber or Concordion, write feature files, and then wonder why their acceptance tests don't prevent production bugs. The issue is usually process: the tests are written by developers alone, after the code, without
Testing
ATDD (Acceptance Test-Driven Development) starts each feature with a failing acceptance test written in collaboration between developers, testers, and product owners. Unlike TDD (which is a solo technical practice) and BDD (which focuses on communication and specification language), ATDD is specifically about driving development from acceptance criteria. The cycle
API Testing
REST APIs have OpenAPI. Event-driven systems — Kafka topics, RabbitMQ queues, WebSocket channels — have AsyncAPI. If your microservices communicate via events and you're not validating message schemas, you're flying blind. A producer changes a field name, drops a required field, or changes a type. Every downstream
Java
async-profiler is a low-overhead sampling profiler for JVM applications that generates flame graphs showing where CPU time is spent. YourKit is a commercial profiler with deeper analysis. This guide covers attaching both profilers to running JVM processes, reading flame graphs, finding memory leaks, and profiling in production environments.
Astro
Astro's hybrid rendering model is one of its most powerful features — you can mix static site generation (SSG) and server-side rendering (SSR) in the same project, choosing per page. A blog index might be static, a user dashboard SSR, and a product page somewhere in between with
Astro
Astro's island architecture is one of its defining features — you get static HTML for most of your page, with interactive components (islands) hydrated only where needed. This composable model is excellent for performance, but it introduces unique testing challenges: when does hydration happen? How do you test client:
Astro
End-to-end tests are where Astro's architecture really gets tested — navigation between pages, form submissions, SSR data loading, hydration of islands, and the subtle differences between your dev server and production build. Playwright is the right tool for this. It's fast, runs in real browsers,
Astro
Astro's component model is unique — .astro files are compiled at build time, which means standard component testing approaches from React or Vue don't apply directly. But with Vitest and Astro's experimental container API, you can write fast, reliable unit tests for your Astro components